STA 102: Intro to Biostatistics

STA 102 is an introductory course in statistics and data science motivated by timely applications from the health sciences, biomedical research, and public health. Students will understand common statistical methods and their suitability in answering specific research questions of interest, conduct rigorous, reproducible analysis using R, interpret results in context and translating them to language accessible to allied health science researchers, and critique statistical usage in the field in order to evaluate data-based claims and decisions.


Course info

All times listed are in US Eastern Time Zone.

Lectures

Section 001      T/Th 3:30 - 4:45PM      See Sakai for Zoom

Labs

Lab 01 (Zheng)      Mon 7:00 - 8:15PM      See Sakai for Zoom

Lab 02 (Korn)      Mon 12:00 - 1:15PM      See Sakai for Zoom

Lab 03 (Roche)      Mon 1:45 - 3:00PM      See Sakai for Zoom

Lab 04      Mon Asynchronous      See Sakai for Zoom



Teaching team and office hours
Instructor Samuel Berchuck Thu 9:00-11:00AM See Sakai for Zoom
Teaching Assistant Kimberly Roche Thu 5:00-7:00PM See Sakai for Zoom
Teaching Assistant Xiaojun Zheng Wed 7:00-9:00PM See Sakai for Zoom
Teaching Assistant Megan Liu Tue 1:00-3:00PM See Sakai for Zoom
Teaching Assistant Rachel Korn Fri 3:00-5:00PM See Sakai for Zoom
Teaching Assistant Kennedy Sun Mon 8:00-10:00AM See Sakai for Zoom

Additional instructor office hours are available by appointment.

Texts and software

No textbooks are required for this course; all lecture notes will be posted to the class website, with accompanying pre-recorded videos posted to the Sakai page in advance of each class meeting. R is the statistical software used in STA 102, with the free user interface R Studio being highly recommended. A free, helpful online resource for R is Wickham and Grolemund, R for Data Science. This course makes heavy use of the tidyverse, a collection of open source R packages.